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from typing import Any, List
from .constants._sync_mode import ASYNC
import random
from ..native import make_native_object
import sys
matx = sys.modules['matx']
class _PosterizeOpImpl:
""" PosterizeOp Impl """
def __init__(self,
device: Any,
bit: int = 4,
prob: float = 1.1) -> None:
self.op: matx.NativeObject = make_native_object(
"VisionPosterizeGeneralOp", device())
self.prob: float = prob
self.bit: int = bit
def __call__(self,
images: List[matx.runtime.NDArray],
bits: List[int] = [],
sync: int = ASYNC) -> List[matx.runtime.NDArray]:
batch_size: int = len(images)
if len(bits) != 0 and len(bits) != batch_size:
assert False, "The length of bits should be equal to input images"
if len(bits) == 0:
bits = [self.bit for _ in range(batch_size)]
if self.prob >= 1.0:
return self.op.process(images, bits, sync)
for i in range(batch_size):
if random.random() >= self.prob:
bits[i] = 8
return self.op.process(images, bits, sync)
[docs]class PosterizeOp:
""" Apply posterization on images. i.e. remove certain bits for each pixel value,
e.g. with bit=4, pixel 77 would become 64 (the last 4 bits are set to 0).
"""
[docs] def __init__(self,
device: Any,
bit: int = 4,
prob: float = 1.1) -> None:
""" Initialize PosterizeOp
Args:
device (Any) : the matx device used for the operation
bit (int, optional): bit for posterization for all images, range from [0, 8], set to 4 by default.
prob (float, optional): probability for posterization on each image. Apply on all by default.
"""
self.op_impl: _PosterizeOpImpl = matx.script(_PosterizeOpImpl)(device=device,
bit=bit,
prob=prob)
[docs] def __call__(self,
images: List[matx.runtime.NDArray],
bits: List[int] = [],
sync: int = ASYNC) -> List[matx.runtime.NDArray]:
""" Apply posterization on images. Only support uint8 images
Args:
images (List[matx.runtime.NDArray]): target images.
bits (List[int]): posterization bit for each image. If not given, the bit for op initialization would be used.
sync (int, optional): sync mode after calculating the output. when device is cpu, the params makes no difference.
ASYNC -- If device is GPU, the whole calculation process is asynchronous.
SYNC -- If device is GPU, the whole calculation will be blocked until this operation is finished.
SYNC_CPU -- If device is GPU, the whole calculation will be blocked until this operation is finished, and the corresponding CPU array would be created and returned.
Defaults to ASYNC.
Example:
>>> import cv2
>>> import matx
>>> from matx.vision import PosterizeOp
>>> # Get origin_image.jpeg from https://github.com/bytedance/matxscript/tree/main/test/data/origin_image.jpeg
>>> image = cv2.imread("./origin_image.jpeg")
>>> device_id = 0
>>> device_str = "gpu:{}".format(device_id)
>>> device = matx.Device(device_str)
>>> # Create a list of ndarrays for batch images
>>> batch_size = 3
>>> nds = [matx.array.from_numpy(image, device_str) for _ in range(batch_size)]
>>> bits = [1, 4, 7]
>>> op = PosterizeOp(device)
>>> ret = op(nds, bits)
"""
return self.op_impl(images, bits, sync)